CORRELATION OF DIFFUSION WEIGHTED MR DETERMINED INFARCT VOLUME WITH ALBERTA STROKE PROGRAM EARLY COMPUTED TOMOGRAPHY SCORE IN ACUTE STROKE PROGNOSIS
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To correlate the Diffusion MRI assessed infarct volume with the ASPECT Score to prognosticate clinical outcome in patients of acute stroke. METHOD AND MATERIALS: This was a cross sectional study comprising 36 patients of acute stroke. Diffusion weighted MR was obtained at b values of 0,500 & 1000 on Siemens Magnetom Skyra 3 Tesla scanner. Diffusion restriction on b=1000 image was measured with VOI tool using manual contouring in each slice. Volume was calculated using the formula (Area x slice thickness) after summating the infarct area measured in each slice. ASPECT Score was assessed on CT in each patient at the time of admission of the patient. Correlation of the infarct volume with ASPECTS was done using student t-test with p < 0.05 considered statistically signicant. ROC curve was used to predict cut off of volume of infarct & ASPECTS predicting adverse patient outcome. RESULTS: There was a statistically signicant inverse correlation between the volume of infarct and ASPECTS (p=0.001; r=-0.844). The AUC for a cut-off of 115 cc of the volume of infarct in predicting adverse patient outcome was 0.931 whereas that for ASPECT Scale of 6 was 0.931. CONCLUSION: Infarct volume correlates well with ASPECT Score, both serving as prognostic tools in predicting patient outcome in acute stroke and having comparable efcacy in predicting prognosis. BACKGROUND: Infarct volume and CT ASPECT Score are resourceful parameters in predicting patient prognosis. Limited studies have been done in the Indian population correlating the Volume of infarct and the CT ASPECT Scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".